Most image denoising algorithms use a basic concept that attempts to perform two opposite goals at the same time: (i) preserve image edges (the spatially correlated high frequency component of the signal) and (ii) smooth out noise (the spatially uncorrelated high frequency component of the signal). A processing step called thresholding or coring is often used to achieve these goals. The operation smooths out the signal variability whose amplitude is smaller than a certain threshold which is considered as a border line to discriminate signal and noise. In other denoising algorithms, such as bilateral filtering, the noise-signal border is defined in a softer way using a Gaussian function to evaluate the variability of a signal within a neighborhood of a pixel. In such algorithms, an appropriate value has to be specified to determine the width of the Gaussian function, and such parameter setting is as important as in the other threshold-based denoising algorithms. If the threshold (or equivalent) in a denoising system is improperly set, the denoising effect may not be satisfactory, or may end up with too much blurred image with over-attenuated edges.
Therefore, appropriate threshold (or equivalent) setting is essential to have satisfactory image quality. It is also important to precisely know the noise amount at the node of signal path where denoising is performed.